Merge pull request #40 from vais-ral/GPU_test

ROF/FGP updated via Cython

2 files changed, 321 insertions, 11 deletions

diff --git a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py b/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py
new file mode 100644
index 0000000..63be1a0
--- /dev/null
+++ b/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py
@@ -0,0 +1,219 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Testing CPU implementation against the GPU one
+
+@author: Daniil Kazantsev
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularizers import ROF_TV, FGP_TV
+
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+def rmse(im1, im2):
+    a, b = im1.shape
+    rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b))    
+    return rmse
+        
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.075
+u0 = Im + np.random.normal(loc = Im ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________ROF-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(1)
+plt.suptitle('Comparison of ROF-TV regularizer using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularization_parameter':0.04,\
+        'number_of_iterations': 1200,\
+        'time_marching_parameter': 0.0025        
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu = ROF_TV(pars['input'],
+             pars['regularization_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+rms = rmse(Im, rof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############ROF TV GPU##################")
+start_time = timeit.default_timer()
+rof_gpu = ROF_TV(pars['input'], 
+                     pars['regularization_parameter'],
+                     pars['number_of_iterations'], 
+                     pars['time_marching_parameter'],'gpu')
+                     
+rms = rmse(Im, rof_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = ROF_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(rof_cpu))
+diff_im = abs(rof_cpu - rof_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(2)
+plt.suptitle('Comparison of FGP-TV regularizer using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularization_parameter':0.04, \
+        'number_of_iterations' :1200 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu = FGP_TV(pars['input'], 
+              pars['regularization_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+             
+rms = rmse(Im, fgp_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############FGP TV GPU##################")
+start_time = timeit.default_timer()
+fgp_gpu = FGP_TV(pars['input'], 
+              pars['regularization_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, fgp_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(rof_cpu))
+diff_im = abs(fgp_cpu - fgp_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+
diff --git a/Wrappers/Python/test/test_gpu_regularizers.py b/Wrappers/Python/test/test_gpu_regularizers.py
index 735a25d..640b3f9 100644
--- a/Wrappers/Python/test/test_gpu_regularizers.py
+++ b/Wrappers/Python/test/test_gpu_regularizers.py
@@ -6,14 +6,13 @@ Created on Tue Jan 30 10:24:26 2018
 @author: ofn77899
 """
 
-
-
 import matplotlib.pyplot as plt
 import numpy as np
 import os    
 from enum import Enum
 import timeit
 from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML
+from ccpi.filters.regularizers import ROF_TV, FGP_TV
 ###############################################################################
 def printParametersToString(pars):
         txt = r''
@@ -52,14 +51,15 @@ u0 = f(u0).astype('float32')
 ## plot 
 fig = plt.figure()
 
-a=fig.add_subplot(2,3,1)
+a=fig.add_subplot(2,4,1)
 a.set_title('noise')
 imgplot = plt.imshow(u0,cmap="gray")
 
 
 ## Diff4thHajiaboli
 start_time = timeit.default_timer()
-pars = {'algorithm' : Diff4thHajiaboli , \
+pars = {
+'algorithm' : Diff4thHajiaboli , \
         'input' : u0,
         'edge_preserv_parameter':0.1 , \
 'number_of_iterations' :250 ,\
@@ -78,7 +78,7 @@ pars['rmse'] = rms
 txtstr = printParametersToString(pars)
 txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
 print (txtstr)
-a=fig.add_subplot(2,3,2)
+a=fig.add_subplot(2,4,2)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
@@ -87,14 +87,15 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(d4h, cmap="gray")
 
-a=fig.add_subplot(2,3,5)
+a=fig.add_subplot(2,4,6)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, 'd4h - u0', transform=a.transAxes, fontsize=12,
          verticalalignment='top', bbox=props)
-imgplot = plt.imshow((d4h - u0)**2, cmap="gray")
+imgplot = plt.imshow((d4h - u0)**2,  vmin=0, vmax=0.03, cmap="gray")
+plt.colorbar(ticks=[0, 0.03], orientation='vertical')
 
 
 ## Patch Based Regul NML
@@ -109,6 +110,7 @@ pars = {'algorithm' : NML , \
 }
 """
 pars = {
+'algorithm' : NML , \
         'input' : u0,
         'regularization_parameter': 0.01,\
         'searching_window_ratio':3, \
@@ -126,7 +128,7 @@ pars['rmse'] = rms
 txtstr = printParametersToString(pars)
 txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
 print (txtstr)
-a=fig.add_subplot(2,3,3)
+a=fig.add_subplot(2,4,3)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
@@ -135,14 +137,103 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(nml, cmap="gray")
 
-a=fig.add_subplot(2,3,6)
+a=fig.add_subplot(2,4,7)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 # place a text box in upper left in axes coords
 a.text(0.05, 0.95, 'nml - u0', transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
-imgplot = plt.imshow((nml - u0)**2, cmap="gray")
+imgplot = plt.imshow((nml - u0)**2,  vmin=0, vmax=0.03, cmap="gray")
+plt.colorbar(ticks=[0, 0.03], orientation='vertical')
 
-plt.show()
         
+        
+## Rudin-Osher-Fatemi (ROF) TV regularization
+start_time = timeit.default_timer()
+pars = {
+'algorithm' : ROF_TV , \
+        'input' : u0,
+        'regularization_parameter': 0.04,\
+        'number_of_iterations':300,\
+        'time_marching_parameter': 0.0025
+        
+	}
+	
+rof_tv = TV_ROF_GPU(pars['input'], 
+                     pars['regularization_parameter'],
+                     pars['number_of_iterations'], 
+                     pars['time_marching_parameter'],'gpu')
+                     
+rms = rmse(Im, rof_tv)
+pars['rmse'] = rms
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(2,4,4)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_tv, cmap="gray")
+
+a=fig.add_subplot(2,4,8)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, 'rof_tv - u0', transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow((rof_tv - u0)**2, vmin=0, vmax=0.03, cmap="gray")
+plt.colorbar(ticks=[0, 0.03], orientation='vertical')
+plt.show()
+
+## Fast-Gradient Projection TV regularization
+"""
+start_time = timeit.default_timer()
+
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularization_parameter':0.04, \
+        'number_of_iterations' :1200 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+fgp_gpu = FGP_TV(pars['input'], 
+              pars['regularization_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, fgp_gpu)
+pars['rmse'] = rms
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(2,4,4)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu, cmap="gray")
+
+a=fig.add_subplot(2,4,8)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, 'fgp_gpu - u0', transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow((fgp_gpu - u0)**2, vmin=0, vmax=0.03, cmap="gray")
+plt.colorbar(ticks=[0, 0.03], orientation='vertical')
+plt.show()
+"""